Author
Abstract
Abstract – Artificial Neural Networks (ANN) can be used as intelligent controllers to
control non-linear dynamic systems through learning, which can easily accommodate
the non linearity’s, time dependencies, model uncertainty and external disturbances.
Modern power systems are complex and non-linear and their operating conditions can
vary over a wide range. The Nonlinear Auto-Regressive Moving Average (NARMAL2)
model system is proposed as an effective neural networks controller model to
achieve the desired robust Automatic Voltage Regulator (AVR) for Synchronous
Generator (SG) to maintain constant terminal voltage. The concerned neural networks
controller for AVR is examined on different models of SG and loads. The results shows
that the neuro-controllers have excellent responses for all SG models and loads in view
point of transient response and system stability compared with conventional PID
controllers. Also shows that the margins of robustness for neuro-controller are greater
than PID controller.
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